Applications of Support Vector Machines in Chemistry

Ivanciuc, Ovidiu

doi:10.1002/9780470116449.ch6

Cited by 318 publications

(197 citation statements)

References 149 publications

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“…SVM is a group of supervised learning methods that can be applied to classification and regression (Ivanciuc, 2007). SVM was originally designed for binary classification (i.e.…”

Section: -Support Vector Machine Proceduresmentioning

confidence: 99%

Multi-class Classifier based on Support Vector Machine with Application to Ordinal Data

Algamal

Allyas

2017

ACAD J NAWROZ UNIV

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Support vector machine initially developed to perform binary classification. This paper presents a multi-class support vector machine classifier and ordinal regression to classify the type of bone mineral density. This paper compares the performance of four multi-class approaches, one-against-all, one-against-one, Weston and Watkins, and Crammer and Singer. Results from our real life data conclude that Crammer and Singer may be better approach depending on training error and the percentage of correctly classified test data. Also, we fined that the training error become more less when the regulization parameter C and kernel parameter  become large.

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“…SVM is a group of supervised learning methods that can be applied to classification and regression (Ivanciuc, 2007). SVM was originally designed for binary classification (i.e.…”

Section: -Support Vector Machine Proceduresmentioning

confidence: 99%

Multi-class Classifier based on Support Vector Machine with Application to Ordinal Data

Algamal

Allyas

2017

ACAD J NAWROZ UNIV

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“…In a short period of time, SVM have found numerous applications in chemistry, such as in drug design [28] when discriminating between ligands and nonligands, inhibitors and non-inhibitors, drug discovery [29], quantitative structure-activity relationships (QSAR, where SVM regression is used to predict various physical, chemical, or biological properties) [30], chemometrics (optimization of chromatographic separation or compound concentration prediction from spectral data as examples), sensors (for qualitative and quantitative prediction from sensor data), chemical engineering (fault detection and modeling of industrial processes) [31]. An excellent review of SVM applications in chemistry can be found in [32].…”

Section: Support Vector Machinesmentioning

confidence: 99%

Improvement of Virtual Screening Predictions using Computational Intelligence Methods

Cano¹,

García-Rodríguez²,

Pérez‐Sánchez³

2013

LDDD

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ABSTRACT:Virtual Screening (VS) methods can considerably aid clinical research, predicting how ligands interact with drug targets. However, the accuracy of most VS methods is constrained by limitations in the scoring function that describes biomolecular interactions, and even nowadays these uncertainties are not completely understood. In order to improve accuracy of scoring functions used in most VS methods we propose a hybrid novel approach where neural networks (NNET) and support vector machines (SVM) methods are trained with databases of known active (drugs) and inactive compounds, being this information exploited afterwards to improve VS predictions.

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“…Support vector machines [12] is considered as a fundamental supervised machine learning technique [13] that has attracted a lot of researchers [14] [15]. Moreover, the SVM is considered among the best classifiers, it is successfully used in many fields such as medical image processing, feature selection for cancer classification [16], protein classification [17], etc.…”

Section: Support Vector Machinesmentioning

confidence: 99%

Support Vector Machines versus Multi-layer Perceptrons for Reducing False Alarms in Intensive Care Units

RejabFahmi¹,

Nouira²,

Trabelsi³

2012

IJCA

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This paper presents a comparative study between two wellknown classification techniques in the machine learning area namely the Multi-Layers Perceptrons (MLP) and the Support Vector Machines (SVM) applied in the medicine field. More precisely, our aim in this paper is to reduce the rate of false alarms in the Intensive Care Units (ICU) using the SVM and the MLP techniques. To this end, we have performed an appropriate setting of parameters for both SVM and MLP techniques to guarantee the good monitoring of patients' states. Then, we have made a comparison between the adapted classification techniques i.e. the SVM and the MLP and the current system using different evaluation criteria. Results of comparative experiments show that the true alarms can be identified with high accuracy by the SVM technique. Compared with the MLP and the current system, the SVM technique shows its potential to reduce the rate of false alarms. General TermsReducing false alarms in ICU using SVM technique.

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Applications of Support Vector Machines in Chemistry

Cited by 318 publications

References 149 publications

Multi-class Classifier based on Support Vector Machine with Application to Ordinal Data

Multi-class Classifier based on Support Vector Machine with Application to Ordinal Data

Improvement of Virtual Screening Predictions using Computational Intelligence Methods

Support Vector Machines versus Multi-layer Perceptrons for Reducing False Alarms in Intensive Care Units

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